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Record W4400258972 · doi:10.20319/icstr.2024.6178

STRUCTURAL ANALYSIS OF A LIGHTWEIGHT ELECTRIC VEHICLE CHASSIS

2024· article· en· W4400258972 on OpenAlexaff
Babatunde Victor Omidiji, A.A. Daniyan, H. A. Owolabi, K. M. Oluwasegun, D. Egbebunmi, Oluwasegun Eso Falodun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsChassisAutomotive engineeringElectric vehicleComputer scienceVehicle safetyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

This study presents a comprehensive investigation into the design and structural analysis of lightweight chassis for electric vehicles. A chassis needs to be able to withstand twist, shock, vibration, and other stresses caused by acceleration, braking, road condition, and shock initiated by other parts of a vehicle. It should carry a maximum load under all operating conditions. Two materials, 304L steel, and Ti-6AL-4V alloy were evaluated, with parameters such as total deformation, equivalent stress, and equivalent elastic strain under consideration. The outcome of the Finite Element Analysis revealed that Ti-6AL-4V can withstand higher stresses than 304L Steel. By investigating the static behaviors of the chassis under static loading due to weight and overload conditions, Ti-6AL-4V was chosen as a suitable replacement for a 304L Steel chassis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.253
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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